In our guide on customer data platforms and real-time interaction management, we discussed the importance of centralised data and leveraging it across different channels. But what comes next? How can you maximise the potential of your data infrastructure and take your customer-centric efforts to the next level?
In this blog, we’ll explore how to use techniques like propensity modelling to build robust marketing intelligence and, ultimately, create superior customer experiences.
Harnessing the Power of Data
With an ever-increasing number of data sources available to marketing organisations, it’s crucial to find ways to extract actionable insights from the vast amounts of information at your disposal. However, manually sifting through each individual record is impractical. That’s where automated processes come in. These processes analyse the data, forecast users’ behaviour, and provide actionable recommendations, unleashing the true power of your data infrastructure.
Propensity Modelling: The Ultimate Forecast
Enter propensity modelling – a statistical technique that predicts the chances of specific events occurring in the future. By harnessing the growing capabilities of machine learning, companies can develop robust propensity models and make highly accurate forecasts. In the realm of marketing, propensity models allow businesses to predict customer behaviour based on their activity history blended with historical and profile data.
Imagine being able to determine whether a customer is likely to respond to a particular offer or purchase a product, given specific circumstances. Understanding customer behaviour allows businesses to fine-tune their marketing efforts and allocate resources more effectively. By integrating propensity modelling into every business process, you transform your organisation into a data-driven, customer-centric powerhouse.
Let’s take a look at some examples of how propensity modelling can drive success within your business.
Inactiveness & churn
Propensity modelling enables you to identify customers who show signs of becoming inactive or are at risk of churning. By detecting these customers in advance, you can take proactive measures to retain their loyalty. This may involve offering personalised incentives, targeted communication, or tailored promotions to re-engage them and keep them actively involved with your brand.
Cross-sell & up-sell
With propensity modelling, you can recommend relevant products or services to customers based on their previous purchases. By analysing their buying patterns and preferences, you can suggest complementary or upgraded items that align with their interests. This not only increases sales but also enhances customer satisfaction by providing them with valuable recommendations tailored to their needs.
Gamification
Propensity modelling allows you to engage customers through gamified experiences. By leveraging data on customer behaviour and preferences, you can create interactive games, challenges, or rewards systems that foster loyalty and encourage repeat business. Gamification adds an element of fun and excitement to the customer experience, increasing engagement and driving long-term brand loyalty.
Engagement
With propensity modelling, you can optimise customer engagement by tailoring your messaging and offers to their preferences. By analysing their past interactions and behaviour, you gain insights into their preferences, interests, and buying habits. Armed with this knowledge, you can deliver personalised messages, targeted offers, and relevant content that resonates with each individual customer. This enhances their experience, makes them feel valued and understood, and ultimately increases their engagement with your brand.
Onboarding
Propensity modelling helps enhance the onboarding process for new customers, delivering a seamless and personalised experience right from the start. By analysing data on previous successful onboarding experiences, you can identify the key factors that contribute to a smooth transition for new customers. This enables you to provide customised onboarding journeys, personalised welcome messages, and tailored recommendations, ensuring that new customers feel valued, supported, and excited about their decision to choose your brand.
Time to convert
Predicting the time it takes for a lead to convert into a customer is made possible through propensity modelling. By analysing historical data on conversion rates and lead behaviour, you can estimate the average time it takes for leads to progress through the sales funnel. This allows you to allocate resources more effectively, focus your efforts on the most promising leads, and streamline your sales process to maximise conversions.
Customer Lifetime Value
Propensity modelling enables you to determine the potential value of a customer over their lifetime. By analysing data on customer behaviour, purchase history, and interactions, you can identify high-value customers who are likely to generate significant revenue in the long term. This information allows you to prioritise your marketing efforts, allocate resources accordingly, and implement strategies to nurture and retain these valuable customers.
Unsubscribes
With propensity modelling, you can identify patterns that lead to customer unsubscribes. By analysing customer data, such as purchase frequency, engagement levels, and interaction history, you can detect early warning signs of potential churn. Armed with this knowledge, you can take proactive measures, such as targeted re-engagement campaigns, personalised offers, or improved customer support, effectively safeguarding your customer base.
Customer Satisfaction Scores
Propensity modelling enables you to measure and improve customer satisfaction levels. By analysing customer feedback, sentiment analysis, and interaction data, you gain insights into their satisfaction levels and pain points. This information allows you to implement targeted improvements, address customer concerns, and deliver exceptional experiences that drive long-term loyalty and ensure your customers remain delighted with your products or services.
Understanding the Limitations
Like any tool, propensity modelling has its limitations.
- It relies on past data, which may not always accurately reflect future events
- If the data used to create the models is not representative of the overall population, bias can creep in
- The accuracy of predictions is heavily reliant on the assumptions made about customer behaviour. Inaccurate assumptions can lead to less reliable predictions
However, despite these limitations, propensity modelling remains an invaluable tool for businesses seeking to better understand and predict customer behaviour.
Integrating Emerging Technologies
In the fast-paced world of digital marketing, integrating emerging technologies into your analytics platform is essential. However, this comes with its own set of challenges. Managing a multitude of raw data sources across various marketing channels can be overwhelming. Especially considering the constant changes in data sources and values. Moreover, many marketing teams lack a dedicated data analyst and, depending on complexity of modelling, a data scientist. This makes it even more difficult to implement and effectively manage these integrations.
Salesforce’s Marketing Cloud Intelligence is a powerful platform designed to tackle the challenges of modern marketing and Salesforce CRM Analytics’ Einstein Discovery reducing dependency on data analyst/scientist roles. With over 150 technologies and channels available for integration, it allows you to unify your marketing data. All without the need for extensive technical expertise.
Check out our new interactive white paper to find out more about how you can build marketing intelligence and create superior customer experiences.
